fdi in india and its growth linkages - Department Of Industrial Policy ...

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SECONDARY DATA-BASED ANALYSIS Based on the availability of data, we computed the E-G-L and E-G-O for 3-digit NIC sectors of production. The E- G-L has been computed for 93 sectors and the E-G-O for 92 sectors of production. Based on the E-G-L values, there are 41 highly agglomerated, 18 moderately agglomerated and 34 non-agglomerated, i.e., dispersed, sectors. The corresponding numbers for E-G-O are 37, 22 and 33, respectively (Table 6.47). We depict the E-G-L of the top 25 (out of 93) sectors that provide the highest employment in FDI-enabled industrial sectors in Table 6.48. These sectors account for 87 per cent of the FDI-enabled employment in our database. Other important sectoral characteristics, including those for FDI-enabled components, have also been reported. It can be observed that 10 out of the 25 top FDI employment sectors have relatively high agglomeration, 9 are relatively dispersed and 6 sectors are moderately agglomerated. Some of the highly agglomerated sectors with high employment in FDI-enabled production units include growing of crops (011), motor parts (343), general purpose machinery (291), medical appliances (331) and transport equipment (359), among others. Examples of dispersed sectors include chemical products (242), dairy products (152), tobacco products (160), basic iron and steel (271) and motor vehicles (341), among others. About 39 per cent of the total FDI employment is generated in the 10 highly agglomerated E-G-L sectors and 36 per cent in the dispersed sectors. FDI plants account for 22 per cent of all the plants (domestic and FDI) in the highly agglomerated E-G-L sectors, with the corresponding value being 18 per cent in the dispersed sectors. The preferred state-level destinations of the top 25 (out of 93) FDI employment-providing sectors is shown in Table 6.49. It may be observed that the highly agglomerated sectors have relatively high employment in their respective top preferred states, with the reverse holding true for the least agglomerated (dispersed) sectors. The E-G-O of the top 25 (out of 92) sectors that produce the highest output in FDI-enabled plants is reported in Table 6.50. These sectors account for 91 per cent of the FDI-enabled output in our database. Other important sectoral characteristics, including those for FDI-enabled components, have also been reported. It is observed that 6 out of the top 25 FDI output sectors have relatively high agglomeration, 11 are relatively dispersed, and 8 sectors are moderately agglomerated. Some of the highly agglomerated sectors with high output in FDI-enabled production units include motor parts (343), general purpose machinery (291), transport equipment (359), and medical appliances (331), among others. Examples of dispersed sectors include chemical products (242), basic iron and steel (271), motor vehicles (341), dairy products (152) and basic chemicals (241), among others. About 17 per cent of the total FDI output is generated in the six highly agglomerated E-G-O sectors and 61 per cent in the dispersed sectors. FDI plants account for 19 per cent of all the plants (domestic and FDI) in highly agglomerated E-G-O sectors, with the corresponding value being 13 per cent in dispersed sectors. The state-level distribution in five major states for the top 25 FDI output-generating industrial sectors is depicted in Table 6.51. It can be observed that the highly agglomerated sectors have a relatively high output in their respective top preferred states, with the reverse holding true for the least agglomerated (dispersed) sectors. 6.5.2 Determinants of Locational Choice of FDI-enabled Plants As already mentioned, while agglomeration estimates have been based on state-level data, the determinants of locational choice have been analysed at the state level as well as the city level. Apart from this, one can also consider variations across geographical regions like north, east, west and south, as well as firm-level characteristics. 69
FDI IN INDIA AND ITS GROWTH LINKAGES While firms are generally headquartered in relatively large cities, the plants are located in large as well as small (Class-3) cities. While investors in industrial plants may not follow a highly structured theory-based model with regard to the choice of a particular location, there would be some rule-of-thumb considerations for these decisions. Multiple factors are likely to play a simultaneous role in when a firm makes a decision on where to locate a plant. The decision would be based primarily on the nature of the plant. For example, an integrated iron and steel plant would prefer to locate close to regions producing primary inputs (iron ore and coal), whereas a cement plant would be close to limestone quarries. State-level factors may include the number of SEZs, energy deficit, per capita income, and the number of workers’ unions. City-level factors may include congestion in a city, presence of a port/ airport/ SEZ, distance from the nearest major city, and the share of FDI plants in total plants, among others. The share of FDI plants in the total number of plants in a city may be taken as a proxy for FDI plant density/ FDI agglomeration. The analysis is based on 1,240 plants located in 286 cities (21 Class-1, 20 Class-2 and 245 Class-3 cities) spread over 26 states and Union Territories (Tables 6.52 and 6.53). These 1,240 plants account for 12 per cent of the total plants (FDI plus domestic) located in the corresponding 286 cities and 26 states / Union Territories. Of the 1,240 FDI plants, 434 plants are located in Class-1 cities, 85 in Class-2 cities and 721 in Class-3 cities. Thus, Class-3 cities account for 58 per cent of the total number of FDI plants. With regard to regional distribution, there are 217 FDI plants in the north, 184 in the east, 481 in the west and 358 in the south (Table 6.54). In the northern region, about 79 per cent of the 217 FDI plants are located in Class-3 cities; the corresponding numbers are 71 per cent in the east, 51 per cent in the west and 49 per cent in the south. In the model estimation, we have divided city sizes as “large” for Class-1 and Class-2 cities taken together, and “small” for Class-3 cities. We have used a discrete choice binary logit econometric model to estimate locational decisions of FDI-enabled plants in India. 15 The discrete choice model facilitates our analysis to reveal the city-level preferences of FDI-enabled firms which might otherwise be lost in aggregate methodologies (Cheng, 2007). In the estimated regression equation, the dependent variable is set up as zero if a particular plant is located in a large city (Classes 1 and 2) and 1 if it is located in a small (Class-3) city. We worked with various alternative model specifications based on independent variables with firm-, city-, state-, and region-specific characteristics. These characteristics relate to dummy, discrete, and continuous variables. In our final estimate, we incorporate the firm-level characteristic as export intensity (continuous). The city-level characteristics include the share of FDI plants in total plants (continuous), airport location in a city (dummy variable) and the distance from the nearest Class-1 city (distance-wise dummies). The state-level variables include the number of SEZs (discrete), power deficit to availability ratio (continuous), number of trade unions (discrete) and per capita income (continuous). Other independent variables refer to the four regions (dummies) as well as regional interaction dummy variables vis-àvis distance dummies. It was found that the alternative specifications make some of the independent variables consistent in terms of sign and significance, while other variables provide fragile results with respect to sign/ significance. Variables, such as the share of FDI plants in a city and electricity deficit to availability ratio in a state, are robust across various model specifications. One variable that showed less robust results is per capita income. However, we included this in the final specification of the model as a proxy for level of development of a state. Two specifications, one showing the base estimate and other depicting the final accepted specification, are shown in Table 6.55. 15.Technical details of this model are provided in Long and Freeze (2003). 70
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FDI IN INDIA AND ITS GROWTH LINKAGE
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PROJECT TEAM Study Team Project Lea
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linkages with the economy. The sect
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Contents Executive Summary Chapter
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Chapter 5: FDI: Linkages with the E
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Executive Summary Foreign direct in
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EXECUTIVE SUMMARY petroleum product
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Chapter 1: Introduction 1.1 Backdro
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section industry data. However, the
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in India stood at $23 billion, show
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viii) Backward and Forward Linkages
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Chapter 2: FDI Scenario in India: T
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Secretariat for Industrial Assistan
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Table 2.2 shows that FDI outflows f
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at about 2 per cent or less of the
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Table 2.5 Sector-wise Break-up of F
Page 38 and 39: Table 2.7 FDI in India in the Servi
Page 40 and 41: Figure 2.4 Country-wise FDI Inflows
Page 42 and 43: 2.10 Foreign Technology Transfers A
Page 44 and 45: Chapter 3: Mergers & Acquisitions a
Page 46 and 47: Table 3.2 Short-term effects of FDI
Page 48 and 49: proportion of foreign investment in
Page 50 and 51: Chapter 4: Special Economic Zones (
Page 52 and 53: 4.4 Management of SEZs SEZs are und
Page 54 and 55: in the eight major SEZ’s exports
Page 56 and 57: Foreign Direct Investment in SEZs S
Page 58 and 59: In a span of a few years, there hav
Page 60 and 61: SPECIAL ECONOMIC ZONES (SEZS) AND F
Page 62 and 63: existing infrastructure, (b) exploi
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Page 70 and 71: construction (4200); fuels, includi
Page 72 and 73: FDI: LINKAGES WITH THE ECONOMY S. N
Page 74: Table 5.5 Sector-wise FDI in India
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Page 92 and 93: Chapter 7: Primary Survey: Analysis
Page 94 and 95: the North and South zones, whereas
Page 96 and 97: PRIMARY SURVEY: ANALYSIS AND RESULT
Page 98 and 99: Table 7.1a Distribution of Surveyed
Page 100 and 101: Table 7.2b Zone-wise Distribution o
Page 102 and 103: Table 7.3b Zone-wise Distribution o
Page 104 and 105: Table 7.6a Number of Employees in S
Page 106 and 107: Table 7.7a Mode of Transport from F
Page 108 and 109: Table 7.8a Number of FDI-enabled Fi
Page 110 and 111: Chapter 8: FDI Data Reporting and C
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Page 114 and 115: three categories, namely, (i) inves
Page 116 and 117: Table 8.1 Trends in FDI Inflows to
Page 118 and 119: FDI DATA REPORTING AND CLASSIFICATI
Page 120 and 121: Table 8.3 Trends in FDI Inflows by
Page 122 and 123: Table 8.5 DIPP Classification of FD
Page 124 and 125: SECTOR-WISE CODE 3700 DEFENCE INDUS
Page 126 and 127: FDI DATA REPORTING AND CLASSIFICATI
Page 128 and 129: S. No. DIPP DIPP Activity NIC 2-Dig
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Chapter 10: Key Findings and Salien
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Findings � About half the total o
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7. Forex Implications: To understan
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manufacturing firms taken together.
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References Australian Bureau of Sta
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for economic growth and poverty all
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Tables for Chapter 6
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Questionnaires
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